Generational Diversity in the Workplace: Challenges and Opportunities for Nursing Education
Bibliographic record
Abstract
The future of the nursing profession foresees challenges such as downsizing, changing skill mixes, and higher acuity patients (LeDuc & Kotzer, 2009; World Health Organization, 2013). Nursing students must be adequately prepared to handle such challenges by understanding their own values, the values of their colleagues, and the values of the collective nursing profession (Hahn, 2011; Hamlin & Gillespie, 2011; LeDuc & Kotzer, 2009). Yet, given the fact that nursing is now highly diversified by generational cohorts, each of whom have their own unique set of values and understanding, relating to fellow nurses and working collaboratively is more difficult than ever (Mangold, 2007). Recognizing generational differences as a potential barrier to quality nursing care and a cause of workplace conflict, educators in the profession have begun to tailor courses and teaching styles to meet the distinct needs of generationally diverse classes and work settings (Faithfull-Byrne, Thompson, Convey, Cross, & Moss, 2015; Hamlin & Gillespie, 2011; Mangold, 2007). To aide in this process, the professional development workshop proposed here will provide educators with an opportunity to learn more about generational diversity and offer strategies to maximize learning for all generations in the nursing field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".